arxiv_cv 90% Match Research Paper Autonomous Driving Researchers,Robotics Engineers,Computer Vision Scientists,Security System Developers 3 weeks ago

CALM-Net: Curvature-Aware LiDAR Point Cloud-based Multi-Branch Neural Network for Vehicle Re-Identification

computer-vision › object-detection

📄 Abstract

Abstract: This paper presents CALM-Net, a curvature-aware LiDAR point cloud-based multi-branch neural network for vehicle re-identification. The proposed model addresses the challenge of learning discriminative and complementary features from three-dimensional point clouds to distinguish between vehicles. CALM-Net employs a multi-branch architecture that integrates edge convolution, point attention, and a curvature embedding that characterizes local surface variation in point clouds. By combining these mechanisms, the model learns richer geometric and contextual features that are well suited for the re-identification task. Experimental evaluation on the large-scale nuScenes dataset demonstrates that CALM-Net achieves a mean re-identification accuracy improvement of approximately 1.97\% points compared with the strongest baseline in our study. The results confirms the effectiveness of incorporating curvature information into deep learning architectures and highlight the benefit of multi-branch feature learning for LiDAR point cloud-based vehicle re-identification.

Authors (3)

Dongwook Lee

Sol Han

Jinwhan Kim

Submitted

October 16, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

CALM-Net is a novel curvature-aware, multi-branch neural network for vehicle re-identification using LiDAR point clouds. It effectively integrates edge convolution, point attention, and a curvature embedding to learn richer geometric and contextual features, outperforming existing baselines.

Business Value

Enhances the ability of autonomous systems and surveillance platforms to track and identify vehicles, improving safety and security in intelligent transportation systems.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Moderate, requires LiDAR sensor data and sufficient computational resources for inference.

Limitations Addressed

Addresses the challenge of learning discriminative features from 3D point clouds for vehicle re-identification, particularly by incorporating curvature information to better characterize local surface variations.

Performance Gains

1.97% improvement in mean re-identification accuracy compared to the strongest baseline

Technical Tags

LiDAR point cloudsvehicle re-identificationmulti-branch neural networkcurvature-awareedge convolutionpoint attentiongeometric featuresdiscriminative features

Research Topics

3D Object RecognitionPoint Cloud ProcessingDeep Learning for VisionSensor Fusion (implied)

Methods & Architectures

CALM-NetMulti-branch architectureEdge convolutionPoint attentionCurvature embedding Multi-branch Neural NetworkConvolutional Neural Networks (CNNs)

Applications & Tasks

Autonomous Driving Robotics Surveillance Smart Cities Vehicle Re-IdentificationFeature Learning from Point CloudsDiscriminative Feature Extraction Vehicle Re-Identification using LiDAR point clouds

Datasets & Benchmarks

Datasets

nuScenes

Mean re-identification accuracy

Related Fields

Computer Vision3D PerceptionMachine LearningRobotics

Keywords

LiDARpoint cloudsvehicle re-identification3Ddeep learningneural networkcurvaturegeometric featuresCALM-NetnuScenes

Academic Context

#3D Object Recognition#Point Cloud Processing#Deep Learning for Vision#Sensor Fusion (implied)

Commercial Potential

Potential Products

Vehicle tracking systemsIntelligent traffic management solutionsAutonomous vehicle perception modules

Target Industries

AutomotiveTransportationSecuritySmart Cities

Use Case Examples

Identifying a specific vehicle that has left a scene and reappears later.Enhancing the perception capabilities of autonomous vehicles for robust tracking.

Competitive Edge

Offers improved accuracy for vehicle re-identification from LiDAR data by leveraging curvature information and a multi-branch architecture.

Market Opportunity

Growing market for advanced driver-assistance systems (ADAS) and autonomous driving technologies.

Revenue Models

Licensing of the CALM-Net technologyintegration into automotive software stacksdevelopment of specialized perception modules.

Resource Requirements

Compute Needs

Moderate to high, depending on the complexity of the point cloud and the network size.

Data Requirements

Requires large-scale LiDAR point cloud datasets with annotated vehicle information.

Deployment Constraints

Real-time processing capability, accuracy in varying weather and lighting conditions, computational cost.

Scalability

Scalability depends on the efficiency of the point cloud processing and the network architecture.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into perception systems.

Patent Potential

Moderate, for the CALM-Net architecture and curvature embedding technique.

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